ICON-MIC: Implementing a CPU/MIC Collaboration Parallel Framework for ICON on Tianhe-2 Supercomputer | |
Wang, Zihao2,3; Chen, Yu2,3; Zhang, Jingrong2,3; Li, Lun2,4; Wan, Xiaohua2; Liu, Zhiyong2; Sun, Fei1,3,5; Zhang, Fa2 | |
刊名 | JOURNAL OF COMPUTATIONAL BIOLOGY |
2017-11-29 | |
页码 | 12 |
关键词 | electron tomography hybrid task allocation strategy ICON MIC acceleration parallel NUFFT Tianhe-2 supercomputer |
ISSN号 | 1066-5277 |
DOI | 10.1089/cmb.2017.0151 |
英文摘要 | Electron tomography (ET) is an important technique for studying the three-dimensional structures of the biological ultrastructure. Recently, ET has reached sub-nanometer resolution for investigating the native and conformational dynamics of macromolecular complexes by combining with the sub-tomogram averaging approach. Due to the limited sampling angles, ET reconstruction typically suffers from the missing wedge problem. Using a validation procedure, iterative compressed-sensing optimized nonuniform fast Fourier transform (NUFFT) reconstruction (ICON) demonstrates its power in restoring validated missing information for a low-signal-to-noise ratio biological ET dataset. However, the huge computational demand has become a bottleneck for the application of ICON. In this work, we implemented a parallel acceleration technology ICON-many integrated core (MIC) on Xeon Phi cards to address the huge computational demand of ICON. During this step, we parallelize the element-wise matrix operations and use the efficient summation of a matrix to reduce the cost of matrix computation. We also developed parallel versions of NUFFT on MIC to achieve a high acceleration of ICON by using more efficient fast Fourier transform (FFT) calculation. We then proposed a hybrid task allocation strategy (two-level load balancing) to improve the overall performance of ICON-MIC by making full use of the idle resources on Tianhe-2 supercomputer. Experimental results using two different datasets show that ICON-MIC has high accuracy in biological specimens under different noise levels and a significant acceleration, up to 13.3x, compared with the CPU version. Further, ICON-MIC has good scalability efficiency and overall performance on Tianhe-2 supercomputer. |
资助项目 | National Key Research and Development Program of China[2017YFA0504702] ; NSFC[U1611263] ; NSFC[U1611261] ; NSFC[61232001] ; NSFC[61472397] ; NSFC[61502455] ; NSFC[61672493] ; Special Program for Applied Research on Super Computation of the NSFC-Guangdong Joint Fund (the second phase) |
WOS研究方向 | Biochemistry & Molecular Biology ; Biotechnology & Applied Microbiology ; Computer Science ; Mathematical & Computational Biology ; Mathematics |
语种 | 英语 |
出版者 | MARY ANN LIEBERT, INC |
WOS记录号 | WOS:000417292900001 |
内容类型 | 期刊论文 |
源URL | [http://119.78.100.204/handle/2XEOYT63/5558] |
专题 | 中国科学院计算技术研究所期刊论文_英文 |
通讯作者 | Liu, Zhiyong; Zhang, Fa |
作者单位 | 1.Chinese Acad Sci, Inst Biophys, Ctr Biol Imaging, Beijing, Peoples R China 2.Chinese Acad Sci, Inst Comp Technol, High Performance Comp Res Ctr, Beijing 100101, Peoples R China 3.Univ Chinese Acad Sci, Beijing, Peoples R China 4.Univ Chinese Acad Sci, Sch Math Sci, Beijing, Peoples R China 5.Chinese Acad Sci, Inst Biophys, CAS Ctr Excellence Biomacromol, Natl Key Lab Biomacromol, Beijing, Peoples R China |
推荐引用方式 GB/T 7714 | Wang, Zihao,Chen, Yu,Zhang, Jingrong,et al. ICON-MIC: Implementing a CPU/MIC Collaboration Parallel Framework for ICON on Tianhe-2 Supercomputer[J]. JOURNAL OF COMPUTATIONAL BIOLOGY,2017:12. |
APA | Wang, Zihao.,Chen, Yu.,Zhang, Jingrong.,Li, Lun.,Wan, Xiaohua.,...&Zhang, Fa.(2017).ICON-MIC: Implementing a CPU/MIC Collaboration Parallel Framework for ICON on Tianhe-2 Supercomputer.JOURNAL OF COMPUTATIONAL BIOLOGY,12. |
MLA | Wang, Zihao,et al."ICON-MIC: Implementing a CPU/MIC Collaboration Parallel Framework for ICON on Tianhe-2 Supercomputer".JOURNAL OF COMPUTATIONAL BIOLOGY (2017):12. |
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